Quantization
Table with columns: Component, Format| Component | Format |
|---|
| MoE and shared-expert MLP weights | W4A16 NVFP4, group size 16 |
| Attention projections | FP8 |
| Language-model output head | W4A16 NVFP4 |
| Vision and other excluded modules | BF16 |
| Native MTP module | BF16 |
The checkpoint was produced with NVIDIA ModelOpt 0.45.0 and is approximately
21.8 GiB across four safetensor shards. Exact layer assignments are recorded in
hf_quant_config.json.
vLLM
Tested with vLLM 0.25.1, FlashInfer attention, the Marlin MoE backend, a BF16
KV cache, and Qwen reasoning/tool parsers.
vllm serve TheShed/BigBang-v1-W4A16-NVFP4-MTP \
--trust-remote-code \
--kv-cache-dtype bfloat16 \
--attention-backend flashinfer \
--moe-backend marlin \
--speculative-config '{"method":"mtp","num_speculative_tokens":1,"moe_backend":"triton"}' \
--reasoning-parser qwen3 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_xml
MTP is optional. To serve the target model without speculative decoding, omit
the --speculative-config argument. A separate non-MTP checkpoint is not
required.
Notes
- The architecture supports up to 262,144 tokens. Usable context and
concurrency depend on available memory and KV-cache configuration.
- Although the quantization metadata advertises FP8 KV cache, BF16 KV was used
for testing because calibrated FP8 KV scales are not included.
- No formal quality, throughput, or multimodal comparisons against the
upstream BF16 checkpoint are provided.
- The upstream model's limitations and safety considerations still apply.
Attribution and license
Released under the Apache License 2.0, matching the upstream model.